The provision of routine and sustained global, regional and local information on the marine environment is not sufficient to meet society’s needs for describing, understanding and forecasting natural marine variability from days to decades, marine responses to climate change (e.g., ocean warming, acidification, and deoxygenation) and other human impacts (loss of biodiversity, pollution), sustainable management of living marine resources (fish stocks) and marine protection. In order to fulfill society’s mandate for ocean observations (e.g. OceanObs‘09, 2010), advanced concepts and routines have to be developed, tested and implemented to ensure sensors efficiently deliver the required information via simulated ocean models. Further the systematic design of global, regional and local ocean observation systems under scientific, technical, legal, and economic constraints need to be addressed.
- ‘From Sensor to information’ addresses issues concerned with observation data processing toward specific projects, such as deep ocean heat content or virtual seafloor reconstruction.
- ‘Improving marine observation networks’ is concerned with system design to optimally meet various scientific requirements with regard to capability, logistical effort, costs and legal and political constraints.
- Integrated multidisciplinary ocean observations in small island states (e.g. Cape Verde)
Seamless Data Workflows for the Cape Verde Ocean Observatory
The Cape Verde Ocean Observatory (CVOO) collects various types of observation data, such as mooring and satellite data. The data is stored and managed in Kiel's Ocean Science Information System, as part of the Kiel Data Management Infrastructure. The research question to be addressed is to investigate all data workflows in CVOO’s context, starting with mobile data acquisition, data quality assurance, data processing for modeling towards data archival and publication.
A more specific research question is how to automatically collect provenance information while the data workflows are executed. The goal is to relieve the scientists from the tedious tasks of the data management, in order to allow to focus the interesting parts of data handling and data analysis.